Overview

Dataset statistics

Number of variables13
Number of observations2774
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory271.0 KiB
Average record size in memory100.0 B

Variable types

Numeric11
DateTime2

Alerts

df_index is highly correlated with days_High correlation
gross_revenue is highly correlated with quantity and 3 other fieldsHigh correlation
quantity is highly correlated with gross_revenue and 2 other fieldsHigh correlation
purchases is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
days_ is highly correlated with df_index and 3 other fieldsHigh correlation
buy_ is highly correlated with gross_revenue and 3 other fieldsHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
df_index is highly correlated with days_High correlation
gross_revenue is highly correlated with quantity and 2 other fieldsHigh correlation
quantity is highly correlated with gross_revenue and 2 other fieldsHigh correlation
purchases is highly correlated with gross_revenue and 2 other fieldsHigh correlation
days_ is highly correlated with df_indexHigh correlation
buy_ is highly correlated with gross_revenue and 2 other fieldsHigh correlation
df_index is highly correlated with days_High correlation
gross_revenue is highly correlated with quantity and 2 other fieldsHigh correlation
quantity is highly correlated with gross_revenue and 2 other fieldsHigh correlation
purchases is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
days_ is highly correlated with df_indexHigh correlation
buy_ is highly correlated with gross_revenue and 2 other fieldsHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
df_index is highly correlated with avg_recency_days and 1 other fieldsHigh correlation
gross_revenue is highly correlated with quantity and 3 other fieldsHigh correlation
recency_in_days is highly correlated with days_High correlation
quantity is highly correlated with gross_revenue and 3 other fieldsHigh correlation
purchases is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_ticket is highly correlated with gross_revenue and 1 other fieldsHigh correlation
avg_recency_days is highly correlated with df_index and 1 other fieldsHigh correlation
days_ is highly correlated with df_index and 2 other fieldsHigh correlation
buy_ is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 51.90076813) Skewed
frequency is highly skewed (γ1 = 46.08548575) Skewed
df_index has unique values Unique
customer_id has unique values Unique
recency_in_days has 34 (1.2%) zeros Zeros

Reproduction

Analysis started2022-05-26 11:11:55.542561
Analysis finished2022-05-26 11:12:45.663403
Duration50.12 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct2774
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2215.728911
Minimum0
Maximum5612
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-05-26T08:12:45.920110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile179.65
Q1879.25
median2017.5
Q33359.75
95-th percentile4878.15
Maximum5612
Range5612
Interquartile range (IQR)2480.5

Descriptive statistics

Standard deviation1501.511553
Coefficient of variation (CV)0.6776603154
Kurtosis-0.954277711
Mean2215.728911
Median Absolute Deviation (MAD)1220.5
Skewness0.3797883099
Sum6146432
Variance2254536.943
MonotonicityStrictly increasing
2022-05-26T08:12:46.148391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
28751
 
< 0.1%
28611
 
< 0.1%
28631
 
< 0.1%
28641
 
< 0.1%
28671
 
< 0.1%
28681
 
< 0.1%
28711
 
< 0.1%
28721
 
< 0.1%
28731
 
< 0.1%
Other values (2764)2764
99.6%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
56121
< 0.1%
56021
< 0.1%
55961
< 0.1%
55711
< 0.1%
55651
< 0.1%
55541
< 0.1%
55531
< 0.1%
55371
< 0.1%
55361
< 0.1%
55271
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct2774
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15285.69971
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.0 KiB
2022-05-26T08:12:46.408348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12626.65
Q113815.25
median15242.5
Q316779.75
95-th percentile17950.35
Maximum18287
Range5940
Interquartile range (IQR)2964.5

Descriptive statistics

Standard deviation1714.984904
Coefficient of variation (CV)0.1121953811
Kurtosis-1.206915065
Mean15285.69971
Median Absolute Deviation (MAD)1483.5
Skewness0.01599078757
Sum42402531
Variance2941173.222
MonotonicityNot monotonic
2022-05-26T08:12:46.658370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
151721
 
< 0.1%
135161
 
< 0.1%
143231
 
< 0.1%
180791
 
< 0.1%
147001
 
< 0.1%
150271
 
< 0.1%
170291
 
< 0.1%
132201
 
< 0.1%
159851
 
< 0.1%
Other values (2764)2764
99.6%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
123591
< 0.1%
123601
< 0.1%
123621
< 0.1%
123641
< 0.1%
123701
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182731
< 0.1%
182721
< 0.1%
182701
< 0.1%
182651
< 0.1%
182631
< 0.1%
182611
< 0.1%
182601
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2760
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2904.649153
Minimum36.56
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-05-26T08:12:46.968471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum36.56
5-th percentile264.557
Q1628.195
median1170.87
Q32423.86
95-th percentile7579.4915
Maximum279138.02
Range279101.46
Interquartile range (IQR)1795.665

Descriptive statistics

Standard deviation10927.08309
Coefficient of variation (CV)3.761928726
Kurtosis331.9637848
Mean2904.649153
Median Absolute Deviation (MAD)690.475
Skewness16.26124122
Sum8057496.75
Variance119401144.8
MonotonicityNot monotonic
2022-05-26T08:12:47.213490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
731.92
 
0.1%
1078.962
 
0.1%
734.942
 
0.1%
1353.742
 
0.1%
178.962
 
0.1%
598.22
 
0.1%
2053.022
 
0.1%
1314.452
 
0.1%
745.062
 
0.1%
379.652
 
0.1%
Other values (2750)2754
99.3%
ValueCountFrequency (%)
36.561
< 0.1%
521
< 0.1%
52.21
< 0.1%
62.431
< 0.1%
68.841
< 0.1%
70.021
< 0.1%
77.41
< 0.1%
84.651
< 0.1%
90.31
< 0.1%
93.351
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
168472.51
< 0.1%
140438.721
< 0.1%
124564.531
< 0.1%
117375.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%

recency_in_days
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct252
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.62797404
Minimum0
Maximum372
Zeros34
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-05-26T08:12:47.485537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median29
Q373
95-th percentile211
Maximum372
Range372
Interquartile range (IQR)63

Descriptive statistics

Standard deviation68.41902268
Coefficient of variation (CV)1.208219503
Kurtosis3.432119089
Mean56.62797404
Median Absolute Deviation (MAD)23.5
Skewness1.898364316
Sum157086
Variance4681.162665
MonotonicityNot monotonic
2022-05-26T08:12:47.807839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
3.6%
487
 
3.1%
385
 
3.1%
285
 
3.1%
876
 
2.7%
1067
 
2.4%
966
 
2.4%
765
 
2.3%
1762
 
2.2%
2255
 
2.0%
Other values (242)2027
73.1%
ValueCountFrequency (%)
034
 
1.2%
199
3.6%
285
3.1%
385
3.1%
487
3.1%
543
1.6%
765
2.3%
876
2.7%
966
2.4%
1067
2.4%
ValueCountFrequency (%)
3721
 
< 0.1%
3661
 
< 0.1%
3601
 
< 0.1%
3583
0.1%
3541
 
< 0.1%
3371
 
< 0.1%
3362
0.1%
3341
 
< 0.1%
3332
0.1%
3301
 
< 0.1%

quantity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1632
Distinct (%)58.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1697.820476
Minimum2
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-05-26T08:12:48.112871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile119
Q1330.25
median699.5
Q31478
95-th percentile4645.5
Maximum196844
Range196842
Interquartile range (IQR)1147.75

Descriptive statistics

Standard deviation6074.430131
Coefficient of variation (CV)3.577781171
Kurtosis438.7272215
Mean1697.820476
Median Absolute Deviation (MAD)449
Skewness17.33919681
Sum4709754
Variance36898701.42
MonotonicityNot monotonic
2022-05-26T08:12:48.365099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
1508
 
0.3%
2468
 
0.3%
2197
 
0.3%
3947
 
0.3%
2007
 
0.3%
2607
 
0.3%
5167
 
0.3%
12007
 
0.3%
3007
 
0.3%
Other values (1622)2698
97.3%
ValueCountFrequency (%)
21
< 0.1%
161
< 0.1%
171
< 0.1%
191
< 0.1%
201
< 0.1%
251
< 0.1%
272
0.1%
301
< 0.1%
321
< 0.1%
332
0.1%
ValueCountFrequency (%)
1968441
< 0.1%
809971
< 0.1%
799631
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
628121
< 0.1%
582431
< 0.1%
577851
< 0.1%

purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct55
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.052992069
Minimum2
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-05-26T08:12:48.817739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median4
Q36
95-th percentile17
Maximum206
Range204
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.071603018
Coefficient of variation (CV)1.498697324
Kurtosis183.9451599
Mean6.052992069
Median Absolute Deviation (MAD)2
Skewness10.62466362
Sum16791
Variance82.29398131
MonotonicityNot monotonic
2022-05-26T08:12:49.138067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2781
28.2%
3498
18.0%
4393
14.2%
5237
 
8.5%
6173
 
6.2%
7138
 
5.0%
898
 
3.5%
969
 
2.5%
1055
 
2.0%
1154
 
1.9%
Other values (45)278
 
10.0%
ValueCountFrequency (%)
2781
28.2%
3498
18.0%
4393
14.2%
5237
 
8.5%
6173
 
6.2%
7138
 
5.0%
898
 
3.5%
969
 
2.5%
1055
 
2.0%
1154
 
1.9%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
0.1%
861
< 0.1%
721
< 0.1%
622
0.1%
601
< 0.1%
571
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct2772
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.33922977
Minimum2.150588235
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-05-26T08:12:49.475324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.150588235
5-th percentile4.852702153
Q112.42654846
median17.94687607
Q325.07465812
95-th percentile88.42744262
Maximum56157.5
Range56155.34941
Interquartile range (IQR)12.64810966

Descriptive statistics

Standard deviation1071.049129
Coefficient of variation (CV)20.46360127
Kurtosis2718.321494
Mean52.33922977
Median Absolute Deviation (MAD)6.333840598
Skewness51.90076813
Sum145189.0234
Variance1147146.236
MonotonicityNot monotonic
2022-05-26T08:12:49.757011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.478333332
 
0.1%
4.1622
 
0.1%
18.152222221
 
< 0.1%
34.631016951
 
< 0.1%
7.6033333331
 
< 0.1%
23.904926471
 
< 0.1%
31.57751
 
< 0.1%
16.807222221
 
< 0.1%
28.755669291
 
< 0.1%
19.43739131
 
< 0.1%
Other values (2762)2762
99.6%
ValueCountFrequency (%)
2.1505882351
< 0.1%
2.43251
< 0.1%
2.4623711341
< 0.1%
2.5112413791
< 0.1%
2.5153333331
< 0.1%
2.651
< 0.1%
2.6569318181
< 0.1%
2.7075982531
< 0.1%
2.7606215721
< 0.1%
2.7704641911
< 0.1%
ValueCountFrequency (%)
56157.51
< 0.1%
4453.431
< 0.1%
1687.21
< 0.1%
952.98751
< 0.1%
872.131
< 0.1%
841.02144931
< 0.1%
651.16833331
< 0.1%
6401
< 0.1%
624.41
< 0.1%
615.751
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1155
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.8018889
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-05-26T08:12:50.047672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q134.24166667
median59
Q399
95-th percentile224
Maximum366
Range365
Interquartile range (IQR)64.75833333

Descriptive statistics

Standard deviation66.51501118
Coefficient of variation (CV)0.8440788934
Kurtosis3.674436164
Mean78.8018889
Median Absolute Deviation (MAD)30
Skewness1.828361094
Sum218596.4398
Variance4424.246712
MonotonicityNot monotonic
2022-05-26T08:12:50.298108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7021
 
0.8%
4618
 
0.6%
5517
 
0.6%
9116
 
0.6%
4916
 
0.6%
3116
 
0.6%
3515
 
0.5%
4215
 
0.5%
2115
 
0.5%
2614
 
0.5%
Other values (1145)2611
94.1%
ValueCountFrequency (%)
19
0.3%
24
0.1%
2.8615384621
 
< 0.1%
36
0.2%
3.3303571431
 
< 0.1%
3.3513513511
 
< 0.1%
45
0.2%
4.1910112361
 
< 0.1%
4.2758620691
 
< 0.1%
4.51
 
< 0.1%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3641
 
< 0.1%
3631
 
< 0.1%
3572
0.1%
3561
 
< 0.1%
3552
0.1%
3521
 
< 0.1%
3512
0.1%
3503
0.1%

max_
Date

Distinct252
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size21.8 KiB
Minimum2016-11-30 00:00:00
Maximum2017-12-07 00:00:00
2022-05-26T08:12:50.575017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:50.888131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

min_
Date

Distinct297
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size21.8 KiB
Minimum2016-11-29 00:00:00
Maximum2017-11-28 00:00:00
2022-05-26T08:12:51.157698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:51.408127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

days_
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct373
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean205.0392934
Minimum2
Maximum374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-05-26T08:12:51.698256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile24.65
Q1108
median212
Q3303
95-th percentile363
Maximum374
Range372
Interquartile range (IQR)195

Descriptive statistics

Standard deviation111.1851779
Coefficient of variation (CV)0.5422627831
Kurtosis-1.217319388
Mean205.0392934
Median Absolute Deviation (MAD)97
Skewness-0.162858992
Sum568779
Variance12362.14379
MonotonicityNot monotonic
2022-05-26T08:12:52.005622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36528
 
1.0%
35126
 
0.9%
35825
 
0.9%
36720
 
0.7%
35220
 
0.7%
35619
 
0.7%
36618
 
0.6%
34418
 
0.6%
21117
 
0.6%
35717
 
0.6%
Other values (363)2566
92.5%
ValueCountFrequency (%)
29
0.3%
34
0.1%
45
0.2%
54
0.1%
63
 
0.1%
72
 
0.1%
87
0.3%
95
0.2%
103
 
0.1%
114
0.1%
ValueCountFrequency (%)
3745
 
0.2%
3738
 
0.3%
3728
 
0.3%
3718
 
0.3%
3706
 
0.2%
36910
 
0.4%
36811
 
0.4%
36720
0.7%
36618
0.6%
36528
1.0%

buy_
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct55
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.052992069
Minimum2
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-05-26T08:12:52.307705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median4
Q36
95-th percentile17
Maximum206
Range204
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.071603018
Coefficient of variation (CV)1.498697324
Kurtosis183.9451599
Mean6.052992069
Median Absolute Deviation (MAD)2
Skewness10.62466362
Sum16791
Variance82.29398131
MonotonicityNot monotonic
2022-05-26T08:12:52.628281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2781
28.2%
3498
18.0%
4393
14.2%
5237
 
8.5%
6173
 
6.2%
7138
 
5.0%
898
 
3.5%
969
 
2.5%
1055
 
2.0%
1154
 
1.9%
Other values (45)278
 
10.0%
ValueCountFrequency (%)
2781
28.2%
3498
18.0%
4393
14.2%
5237
 
8.5%
6173
 
6.2%
7138
 
5.0%
898
 
3.5%
969
 
2.5%
1055
 
2.0%
1154
 
1.9%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
0.1%
861
< 0.1%
721
< 0.1%
622
0.1%
601
< 0.1%
571
< 0.1%

frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1225
Distinct (%)44.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04969219171
Minimum0.005449591281
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2022-05-26T08:12:52.947958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.008746355685
Q10.01575839204
median0.0243902439
Q30.04166666667
95-th percentile0.1153846154
Maximum17
Range16.99455041
Interquartile range (IQR)0.02590827462

Descriptive statistics

Standard deviation0.3375950011
Coefficient of variation (CV)6.793723309
Kurtosis2296.521881
Mean0.04969219171
Median Absolute Deviation (MAD)0.01069454458
Skewness46.08548575
Sum137.8461398
Variance0.1139703848
MonotonicityNot monotonic
2022-05-26T08:12:53.194861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.062517
 
0.6%
0.0277777777817
 
0.6%
0.0238095238116
 
0.6%
0.0909090909115
 
0.5%
0.0833333333315
 
0.5%
0.0344827586214
 
0.5%
0.0294117647114
 
0.5%
0.0212765957413
 
0.5%
0.0192307692313
 
0.5%
0.0357142857113
 
0.5%
Other values (1215)2627
94.7%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
0.1%
0.005665722381
 
< 0.1%
0.0056818181822
0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
171
 
< 0.1%
31
 
< 0.1%
21
 
< 0.1%
1.1428571431
 
< 0.1%
18
0.3%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.5508021391
 
< 0.1%
0.53351206431
 
< 0.1%
0.53
 
0.1%

Interactions

2022-05-26T08:12:41.806557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:09.657888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:12.940788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:16.416241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:19.813737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:22.371875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:25.234884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:27.980988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:30.598125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:34.358078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:38.263538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:42.093170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:10.074199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:13.198685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:16.698132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:20.021026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:22.613115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:25.468083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:28.218322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:30.863059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:34.622720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:38.553130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:42.378293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:10.408024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:13.458413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:16.937900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:20.268111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:22.878006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:25.752800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:28.443220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:31.093032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:34.977655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:38.868513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:42.668286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:10.677209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:13.748363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:17.398109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:20.529423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:23.098137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:25.987557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:28.684273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:31.315194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:35.273040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:39.192995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:43.002999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:10.932586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:14.048274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:17.747825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:20.759916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:23.372215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:26.231191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:28.917680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:31.648378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:35.547917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:39.487854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:43.318257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:11.212742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:14.279129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:18.032454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:20.994890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:23.648057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:26.467291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:29.175024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:32.152834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:35.923108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:39.818056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:43.548223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:11.440921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:14.507094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:18.307612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:21.228656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:23.957891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:26.778333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:29.397892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:32.505169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:36.248037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:40.143313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:43.807850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:11.653288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:14.713755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:18.522393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:21.438032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:24.178124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:26.997591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:29.627836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:32.777722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:36.533068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:40.422637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:44.046988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:11.878382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:14.943417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:18.747644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:21.673368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:24.417929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:27.228124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:29.877354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:33.098057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:37.358003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:40.752619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:44.327858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:12.134608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:15.201259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:18.994177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:21.898114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:24.692991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:27.447212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:30.088255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:33.626164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:37.627947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:41.137610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:44.589813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:12.481206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:15.962683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:19.220897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:22.139488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:24.965734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:27.737507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:30.357984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:34.017526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:37.988142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T08:12:41.438085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-26T08:12:53.432996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-26T08:12:53.767926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-26T08:12:54.101169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-26T08:12:55.027421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-26T08:12:45.022055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-26T08:12:45.495356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_in_daysquantitypurchasesavg_ticketavg_recency_daysmax_min_days_buy_frequency
00178505391.2137217333418.1522221.0000002016-11-302016-11-2923417.000000
11130473232.59561390918.90403552.8333332017-10-122016-11-2931890.028302
22125836705.38250281528.90250026.5000002017-12-052016-11-29372150.040323
3313748948.2595439533.86607192.6666672017-09-032016-11-2927950.017921
4415100876.00333803292.00000020.0000002017-01-082016-11-294130.073171
55152914623.302521021445.32647126.7692312017-11-122016-11-29349140.040115
66146885630.87736212117.21978619.2631582017-11-302016-11-29367210.057221
77178095411.911620571288.71983639.6666672017-11-212016-11-29358120.033520
881531160767.900381949125.5434644.1910112017-12-072016-11-29374910.243316
99160982005.6387613729.93477647.6666672017-09-112016-11-2928770.024390

Last rows

df_indexcustomer_idgross_revenuerecency_in_daysquantitypurchasesavg_ticketavg_recency_daysmax_min_days_buy_frequency
2764552717290525.24340425.14941213.02017-12-042017-11-211420.142857
276555361478577.401084225.8000005.02017-11-272017-11-22620.333333
2766553717254272.44425222.43250011.02017-12-032017-11-221220.166667
2767555317232421.522203211.70888912.02017-12-052017-11-231320.153846
2768555417468137.0010116227.4000004.02017-11-272017-11-23520.400000
2769556513596697.04540624.1990367.02017-12-022017-11-25820.250000
27705571148931237.859799216.9568492.02017-11-282017-11-26320.666667
2771559614126706.137508347.0753333.02017-11-302017-11-27430.750000
27725602135211092.39173332.5112414.52017-12-062017-11-271030.300000
2773561215060301.84826242.5153331.02017-11-292017-11-28242.000000